Vinod Kashyap presented on the impact of technology on the auditing profession. He discussed how technologies like artificial intelligence, robotic process automation, audit data standards, and blockchain are transforming auditing. Key points included: - AI is being used for tasks like automated audit processes, deep learning for image recognition, and fraud detection. It allows auditors to leverage more data points. - Technologies help access and verify information more efficiently through tools like RPA, data analytics, and encryption. This impacts tasks like confirmations and testing. - The IAASB is exploring how disruptive technologies like continuous auditing and remote work may further impact the profession. Auditing remains dependent on professional judgement however.

1. Impact of Technology on the Profession:
Human Vs. AI +Bot?
Presented by:
Vinod Kashyap
Head of Indian Delegation, ISO/TC 295 “Audit Data Services”
Member, ISO/TC 307 “Blockchain and DLT Systems”
The Institute of Internal Auditors
India – Kolkata Chapter
29th Annual Conference
2nd April 2022

2. Auditing profession has come a long way from “holler and tick” approach where one person from audit team
would call from the ledger and the other person would tick and then the search will be made for unticked
transactions. Today, technology is being used in auditing by all Big 4 and other top accounting firms.
Executive Summary
ARTIFICIAL INTELLIGENCE
ROBOTIC PROCESS AUTOMATION
AUDIT DATA STANDARDS
AUDIT DATA ANALYTICS
INTELLIGENT PROCESS AUTOMATION
BLOCKCHAIN
Trends that drive digital transformation of auditing

3. 2nd Disruptive Technologies Round Table of International Auditing and Assurance Standards Board (IAASB)
held on 9th February 2022 is part of its ongoing exploration of disruptive technologies. These technologies are
already impacting the audit and assurance profession and have potential for further and greater impact.
Agility
• Data Standardization
• IAASB Role
• ERP & Accounting Systems
GOOD QUALITY DATA
• More opportunities to
auditors to leverage more
data points in audit
MORE DATA SOURCES
• Technologies applied by the
entity to support Internal Control
System
TECHNOLOGY APPLIED
• Technology is not a substitute of
professional judgement & skeptism
PROFESSIONAL SKEPTISM
IAASB Disruptive Technologies Round Table: Key Takeaways
Source : IAASB

4. Innovations are Happening Across Audit and Assurance
Accessing Information & Data
• NLP & Computer Vision
• AI
• API access to external data
• Data Standardization
• IoT Networks for asset
monitoring and data
generation
• New Imaging tools
• Blockchain
Verifying Information
• RPA for executing repetitive
tasks
• Analytics for accessing data ,
and error and fraud detection
• AI powered advanced analytics
for risk detection
Protecting Information
• Virtual Data Rooms for sharing
sensitive information
• Data Anonymisation
• Homomorphic Encryption
Assessing Internal Controls
• Research and ideation
• Product development
• Product launch
Source : IAASB
Unifying Themes
Other Relevant Trends
Continuous and Real Time Audit
and Assurance
Remote Audit and Assurance
Analytics –based Audit and Assurance
Technology -based Audit and Assurance

5. Artificial Intelligence (AI)
Artificial
Intelligence
Machine
Learning
Deep
Learning
Artificial Intelligence
is the study of devices that perceive their environment
and define a course of action that will maximize its
chances of achieving a given goal.
Machine Learning
is the subset of study of artificial intelligence, in which
machines learn how to complete a certain task
without being explicitly programmed to do so.
Deep Learning
is a subset of machine learning in which the tasks are
broken down and distributed into machine learning
algorithms that are organized in consecutive layers.
Each layer builds up from the output of the previous
layer. Together the layers constitute an artificial neural
network that mimics the distributed approach to
problem solving carried out by neurons in a human
brain.

6. AI Enabled Automated Audit Process
• AI collects and analyze
Big Data
• Data related to the
client’s organizational
structure, operational
methods, and
accounting & financial
system fed into AI System.
Pre-Planning
• AI uses the estimate of
the risk level from phase 1
and calculate the audit
fees and the number of
hours
• AI analyzes the database
of contracts and
prepares the contract
• Auditor and client sign
the contract.
Contracting
• Feed flow chart, questionnaire
answers and narratives into the
AI system and use image
recognition and text mining to
analyze them
• Use visualization & pattern
recognition to identify risk
factors
• AI aggregates all the data to
identify fraud & risk factors.
Internal Controls & Risk Factors
• Continuous control
monitoring systems examine
controls continuously
• AI runs process mining to
verify proper Internal
Controls implementation
• Logs are automatically
generated to ensure their
integrity
Control Risk Assessment
• Continuous Data Quality
Assurance ensures quality of
data and assurance
• AI examines data
provenance
• Continuous Test on details of
transactions on 100% of the
population
• Continuous Test on details of
balance
Substantive Tests
• AI uses a Predictive
Model to estimate the
various risks identified
• Audit Report can be
continuous (grade 1-
100 for example)
rather than
categorical (clean,
qualified, adverse etc.
Audit Report

7. Deep Learning in Audit
Deep Learning
in Image
Recognition
Deep Learning
in Speech
Recognition
Deep Learning
in Language
Analysis
Deep Learning
in Natural
Language
Classification
Sensor data, visual recognition
techniques are capable of
recognizing object categories
e.g.: product images, analyse
inventory images captured by
automated drones to replace
physical inventory checks
Streamed or recorded audio
like phone calls, interview
conversations, and
management presentations.
e.g.: voice interactivity can
transform speech into
searchable and analysable
audio data
Analyse text, including both
HTML/text documents and
webpages “ text mining”.
e.g.: extract attributes from
conference call transcripts,
management discussion and
analysis sections of 10-Ks, and
other text data for audit
analytics
Classify earnings conference
call transcripts into two groups:
‘‘fraudulent’’ and ‘‘non-
fraudulent.’’
e.g.: language classification
model to analyse forum posts,
comments, conversations from
social media to obtain
supplemental audit evidence.

8. MACHINE
LEARNING
BIG DATA
NATURAL
LANGUAGE
GROWTH DRIVERS
• Classifier (Tax
Transaction, Contact)
• Fraud Detection
• Review full population
of outliers
• Large scale data
analysis
• Discover facts and
relations that are
difficult for the
human mind
• Synthesis of text
e.g. review of
contacts, vendor
invoices, emails,
transcribed
conversation
Current AI Implementations
MACHINE
VISION
INTELLIGENT
RPA
SPEECH
RECOGNIZATION
GROWTH DRIVERS
• OCR + Machine
Learning to extract
data from images
• Drones + IoT to
perform inventory
inspection
• Test of transactions
• Document work
papers
• GL review
• Bank confirmations
• Decode
conversation
• Chat Bots
• Digital Assistants
All Big Four Accounting Firms are reporting the use of these AI Blocks
Source : Ivy Munoko

9. Current AI implementation Cont.…
Ensemble AI Combines Machine Learning with
domain expertise / business rules and statistical
methods to :
• Gain actionable insights into large population
of data
• Review 100% of data
• Sample from outliers
• Perform risk assessment
Source : Ivy Munoko
Small and Medium Size Accounting Firms are using AI SaaS

10. The challenging situation we face
o In the era of information, electronic data
has been the essential base for audit
work
o The process of auditing data utilization
generally be divided into several steps:
audit data collection, audit data
cleaning & processing, audit data
analysis etc.
o As depicted in the figure audit data
collection is the start point of audit data
utilization
General process of audit data utilization
Business
System
Output from
Business
System
Import data
into audit
system
Audit system
Exchange File
Oracle
SQL Server
DB 2
My SQL
Database
OUTPUT IMPORT
Oracle SQL Server
Database
Source : SAC

11. The challenging situation we face cont.…
o Many different systems to deal with
• Different data structures
• Different data definitions
• Different formats (Date Formats?)
Example Date to show the difference between information
Date : yyyy.mm.dd / mm.dd.yyyy / dd. mm. yyyy
Sales Order
Sales Order ID ….. Fiscal Year Order Transaction Amount Sales Order Date ……
ABC1648324 2021-22 1,20,000/- 10-11-2021
o Difficulty for the user to identify the data they
need
o Different audit areas
o Different techniques for audit analysis
October 11, 2021 November 10, 2021

12. Content of Audit Data Collection Standard
ISO:21378 2019
ISO 21378-2019 covers the areas of General Ledger (GL), Accounts Receivable (AR), Sales, Accounts
Payable (AP), Purchase, Inventory, and Property, Plant and Equipment (PPE)
Purchase
Module
AP Module
Payment information
Purchase invoices
Voucher delivery
Inventory
Module
Sales Module
Purchase Warehouse
Voucher
Sales delivery
voucher
GL Module
PPE Module AR Module
Voucher delivery
PPE purchase
Voucher delivery
Voucher delivery
Sales invoices
Receipt information
Key Components
Modules of ADCS
Connections and
lines
Process flow

13. Intelligent Process Automation (IPA) in Audit
Source : Dr. Abigail Zhang
IPA is a constellation of technologies that expands RPA by augmenting it with AI, cognitive
technologies, and other technologies to make the process automation scalable, flexible,
and intelligent.
Intelligent Process Automation
RPA Artificial Intelligence Other Technologies
v
Cognitive
Computing
v v v v v
v v v v
Machine
Learning
Computer
Vision
Virtual
Agent
NLP/
NLG
Others
Data
Analytics
Drones
Smart
Contracts
IoT Others

14. The “Sense Think Act” Loop of IPA
AI and
cognitive
technologies
Human
Think
Computer vision, natural
language processing, drones,
IoT, etc.
Sense
RPA to execute workflows, APIs,
computer command scripts,
mechanical robotics
Act
Human is in the loop
Source : Dr. Abigail Zhang
Adapted from UiPath (2017)

15. Types of Audit Tasks
Type Examples
Structured
a. Go to the Morningstar website, search for a particular fund name, copy paste the fund
returns to Excel, compare with the return provided by the client
b. Go to the third-party website, input user name and password, then download the
confirmation report
c. Reconciliations, recalculations, confirmations in substantive testing
Semi-structured
a. Read the supporting documents and choose “yes” or “no” on audit planning work paper
b. Go through the pension contract and extract the key term such as eligibility, and then
execute the corresponding testing queries.
Unstructured
a. Fair value estimation
b. Internal control risk assessment
c. Judgements on certain exceptions identified
Source : Dr. Abigail Zhang

16. Steps in Implementing IPA in Audit
Is the task structured,
semi-structured or
unstructured?
Is the audit task
automatable?
If it is automatable, can
or will it be
automated?
If the task will be
automated, what
potential automation
tools that can be used
for this task?
Is there any need to
redesign the process
for more efficient
automation?
Prototyping and
implementing
Source : Dr. Abigail Zhang

17. The “Auditor-in-Loop” in IPA Eco-system
Source : Dr. Abigail Zhang

18. Blockchain
A Blockchain is a decentralized ledger of all transactions in the network. Using Blockchain technology,
participants in the network can confirm transactions without the need for a trusted third party
intermediary.
Someone in a network requests
a transaction
The transaction is broadcast to other
computers (nodes) in the network
The transaction is complete
The network nodes validates the
transaction using agreed algorithms
Smart contacts
(additional business
logic) could be
applied, if needed.
Private Key
The new block is added to the network’s
Blockchain, in a way which is permanent
and unalterable
The verified transaction is combined
with other transactions to create a new
block of data for the ledger

19. Five Key Elements of Blockchain
A complete blockchain incorporates all five of these design elements to authenticate users, validate transactions, and
record that information to the ledger in a way that can’t be corrupted by a single participant or changed after the fact.
Encryption Immutability Distribution Decentralization Tokenization
Source : Gartner

21. Auditing Blockchain
What does this mean for Auditor?
...Is this the end of audit ?
…Am I going to be out of job ?

22. THANK YOU
Questions?